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Comment by scott00 | Hacker News Reader

Been a flurry of self driving car safety news lately. Both Cruise and Waymo are reaching for more sophisticated comparison samples than nationwide stats to demonstrate the safety of their systems.

But the obvious way to prove this, to me anyway, is to run a randomized controlled trial. Put them in a dispatch system with human driven cars, randomize whether any given assignment goes to a human or a robot, and you've got the statistical gold standard.

Anybody understand why they're not doing this?

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Because accidents are rare and drivers are expensive so that might be the world's most expensive study to get to a statistically meaningful number.

It's easy enough to simply compare with existing Uber in the same city along the same roads and get something nearly as accurate for virtually none of the cost.

Drivers doing nothing productive are expensive, but drivers working ride hail are somewhere in the range of cheap to slightly profitable.

The comparison with Uber depends strongly on how similar the dispatch profiles are, and I would not be quick to assume that they are similar. If they are limiting the self-driving cars due to weather, or time of day, or any property of trip type it could easily have a substantial impact.

I believe Waymo cars are now part of the Uber network. So hopefully soon that data should be available. Hopefully it is released.

https://waymo.com/blog/2023/05/waymo-and-uber-partner-to-bri...

Out of curiosity, what would you measure? Accidents? Injuries? The power might be too low to get good data on rare events.

The existing studies have decent metrics given the sample sizes, IMO, I would use something similar. For the Cruise study that was recently released it was collisions, with sub-analyses for collisions with stationary objects, low speed collisions, and high speed collisions.

I believe that you are correct that scale is too low for analyzing injuries or deaths.

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